For older adults, falling is a major concern. It’s the leading cause of injury for those over 65 and the consequences can be life-threatening.
“Falling itself isn’t the issue—it’s the injury that’s really harmful for older adults,” says Syracuse University exercise science professor Yaejin Moon, who lost two of her grandparents to fall-related injuries.
The experience of losing a family member, friend or neighbor from complications after a fall is all too universal. That’s why Moon and Ph.D. student Reese Michaels G’24 are using cutting-edge research tactics—combining advanced artificial intelligence (AI) video analysis with traditional lab research—to learn how people fall and how to prevent serious injury.
Analyzing Falls With AI and Custom Code
AI-powered tools like OpenPose and WHAM are replacing traditional motion-tracking markers, allowing researchers to study movement more easily in real-world settings.
Traditionally, studying human movement meant attaching motion-tracking markers to the body—a technique common in gaming, film and movement science. Today, however, advances in AI make it possible to analyze movement directly from standard video footage.
“If we take a video—even from an iPhone—and input it into the system, the AI can automatically detect key body points and track motion. We don’t need markers anymore,” explains Moon, referring to AI-based pose estimation algorithms such as OpenPose.
Working with researchers in Canada, Moon and Michaels have access to over 1,700 real-life fall videos from surveillance footage in long-term care facilities and hospitals. Using OpenPose and Michaels’ custom code, the research pair track body position and extract biomechanical data to identify which types of falls result in injury and evaluate which movements protect against harm.
“It’s like having access to a black box for accidents,” Moon says. “We can analyze exactly what happened.”
Although Michaels had no prior coding experience, he took a graduate-level Python course through Syracuse’s School of Information Studies. “It was trial by fire, but I was able to write code for one of our projects, and I realized I could apply those skills in a meaningful way to research,” says Michaels, who started working with Moon in the Falk College of Sport as an exercise science master’s student two years ago.
“He can calculate things like velocity of the fall, acceleration and knee angle at the moment of impact—very specific biomechanical outcomes—all generated through his own programming,” Moon says.
As the AI models continue to improve, the team’s research also advances. “These newer AI models can track movement in three dimensions rather than two,” Michaels explains. “That gives us much more insight into things like joint angles during a fall, which opens the door to more realistic and accurate analysis.”
“The goal is to implement this kind of technology in long-term care settings to get real-time insights into how people move and how injuries happen,” Michaels says.
Lab Simulations
Falk College professor Yaejin Moon (left) uses a special treadmill to simulate sudden loss of balance, while motion-capture cameras track how participants respond.
In the lab, the AI models are validated using a specialized treadmill that safely simulates balance loss. The treadmill can move forward, backward and side to side while participants wear a safety harness and adjust to the sudden changes in movement. Motion-capture cameras record every step and reaction.
Falls happen in three phases: the initial phase (standing or walking normally), the loss-of-balance phase (when the fall begins) and the impact phase (when the body hits the ground).
New AI models allow researchers to track movement in 3D, greatly improving the accuracy and realism of fall analysis.
“The perturbation treadmill is used to study that second phase—the moment when balance is lost,” Moon says. “We analyze how people react to losing balance and how they try to recover.”
The research also explores dual-task conditions—how cognitive load impacts the ability to recover balance. Participants are asked to perform mental tasks, such as listing animals or counting backward from 100 by sevens, while walking. This adds a layer of realism, simulating situations where older adults might be distracted by thinking, talking or multitasking while moving.
“Do we recover balance faster when we’re focused solely on walking? Or is our response slower or different when our attention is divided?” Moon inquires.
Research in the Real World
Ph.D. student Reese Michaels G’24 is the lead author of two studies—one published in Scientific Reports and another currently under review in the Journal of Biomechanics.
So, how will this ongoing research impact people’s everyday lives? Moon breaks it down into three key components: “First is understanding the mechanisms—how the body and mind work together during a fall. Second is developing intervention programs. And third is improving technology.”
Michaels, who is now in his second year of the exercise science Ph.D. program, is especially focused on improving technology.
A third-degree black belt in Taekwondo, Moon began her research by teaching older adults how to fall safely using martial arts. Now, she and Michaels are using AI tools to better understand falls and develop new ways to prevent serious injuries.
“One of our next steps is feeding outputs from pose estimation models into a machine learning algorithm that could predict impact force—how hard someone hit the ground,” explains Michaels. “That would give us a direct measure of whether a fracture or injury occurred.”
The pair is also working to make their video analysis methods more generalizable. With ongoing AI advancements and more real-world video data, the team hopes to analyze situations that can’t be replicated in a lab, such as falls down a set of stairs, and to address different age and health groups.
By combining AI, biomechanics and real-world data, this research is not only advancing the study of falls but also laying the foundation for innovative solutions to prevent injuries in aging populations. As technology continues to evolve, their work promises to lead to more precise strategies that could significantly reduce the risks older adults face, ultimately improving their quality of life and safety.
